Mid-Century Ensemble Regional Climate Change Scenarios for the Western United States

To study the impacts of climate change on water resources in the western U.S., global climate simulations were produced using the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Parallel Climate Model (PCM). The Penn State/NCAR Mesoscale Model (MM5) was used to downscale the PCM control (20 years) and three future(2040–2060) climate simulations to yield ensemble regional climate simulations at 40 km spatial resolution for the western U.S. This paper describes the regional simulations and focuses on the hydroclimate conditions in the Columbia River Basin (CRB) and Sacramento-San Joaquin River (SSJ) Basin. Results based on global and regional simulations show that by mid-century, the average regional warming of 1 to 2.5 °C strongly affects snowpack in the western U.S. Along coastal mountains, reduction in annual snowpack was about70% as indicated by the regional simulations. Besides changes in mean temperature, precipitation, and snowpack, cold season extreme daily precipitation increased by 5 to 15 mm/day (15–20%) along theCascades and the Sierra. The warming resulted in increased rainfall at the expense of reduced snowfall, and reduced snow accumulation (or earlier snowmelt) during the cold season. In the CRB, these changes were accompanied by more frequent rain-on-snow events. Overall, they induced higher likelihood of wintertime flooding and reduced runoff and soil moisture in the summer. Changes in surface water and energy budgets in the CRB and SSJ basin were affected mainly by changes in surface temperature, which were statistically significant at the 0.95 confidence level. Changes in precipitation, while spatially incoherent, were not statistically significant except for the drying trend during summer. Because snow and runoff are highly sensitive tospatial distributions of temperature and precipitation, this study shows that (1) downscaling provides more realistic estimates of hydrologic impacts in mountainous regions such as the western U.S., and (2) despite relatively small changes in temperature and precipitation, changes in snowpack and runoff can be much larger on monthly to seasonal time scales because the effects of temperature and precipitation are integrated over time and space through various surface hydrological and land-atmosphere feedback processes. Although the results reported in this study were derived from an ensemble of regional climate simulations driven by a global climate model that displays low climate sensitivity compared with most other models, climate change was found to significantly affect water resources in the western U.S. by the mid twenty-first century.     

A Projection of the Effects of the Climate Change Induced by Increased CO2 on Extreme Hydrologic Events in the Western U.S.

The effects of increased atmospheric CO₂ on the frequency of extreme hydrologic events in the Western United States (WUS) for the 10-yr period of 2040–2049 are examined using dynamically downscaled regional climate change signals. For assessing the changes in the occurrence of hydrologic extremes, downscaled climate change signals in daily precipitation and runoff that are likely to indicate the occurrence of extreme events are examined. Downscaled climate change signals in the selected indicators suggest that the global warming induced by increased CO₂ is likely to increase extreme hydrologic events in the WUS. The indicators for heavy precipitation events show largest increases in the mountainous regions of the northern California Coastal Range and the Sierra Nevada. Increased cold season precipitation and increased rainfall-portion of precipitation at the expense of snowfall in the projected warmer climate result in large increases in high runoff events in the Sierra Nevada river basins that are already prone to cold season flooding in todays climate. The projected changes in the hydrologic characteristics in the WUS are mainly associated with higher freezing levels in the warmer climate and increases in the cold season water vapor influx from the Pacific Ocean.     

Relative impacts of increased greenhouse gas concentrations and land cover change on the surface climate in arid and semi-arid regions of China

Four dynamical downscaling simulations are performed with different combinations of land cover maps and greenhouse gas (GHG) levels using the Weather Research and Forecasting (WRF) model nested in the Community Earth System (CESM) model. A pseudo-global warming downscaling method is used to effectively separate the anthropogenic signals from the internal noises of climate models. Based on these simulations, we investigate the impacts of anthropogenic increase in GHG concentrations and land use and land cover change (LULCC) on mean climate and extreme events in the arid and semi-arid regions of China. The results suggest that increased GHG concentrations lead to significant increases in the surface air temperature at 2 m height (T2m) by 1–1.5 °C and greater increase in the warm day temperature (TX90p) than the cold day temperature (TX10p) in the arid and semi-arid regions. Moreover, precipitation increases by 30–50% in the arid region in cold season (November to March) due to the GHG-induced increase in moisture recycling rate and precipitation efficiency. LULCC leads to significant decreases in the T2m, TX90p, and TX10p by approximately 0.3 °C. The regional LULCC accounts for 66 and 68% decrease in T2m in warm and cold seasons, respectively. The rest changes in T2m results from the changes in lateral boundary condition induced by the global LULCC. In response to LULCC, both the warm and cold day temperatures show a significant decrease in cold seasons, which primarily results from the regional LULCC. LULCC-induced changes in precipitation are generally weak in the arid and semi-arid regions of China.     

2008年初呼和浩特地区持续低温、降雪成因分析

利用常规气象观测资料、以及国家气候中心的海温监测资料等,对2008年初呼市地区出现低温降雪天气的强度、范围、持续时间和灾害影响作了初步分析,并与历史同期进行了比较。分析表明:此次低温降雪天气是在近年来最严重的一次"拉尼娜"事件背景下发生的,与欧亚地区持续大气环流异常密切相关,在"北脊南槽"和西太平洋副高偏北偏强的形势下,北方冷空气不断南下,同时印度洋和孟加拉湾暖湿气流源源不断地往北到东北方向输送,冷暖气流在我国中东部地区频繁交汇,造成了我市持续低温、降雪异常偏多。     

Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves.     

The contribution of snow condition trends to future ground climate

Global climate models predict that terrestrial northern high-latitude snow conditions will change substantially over the twenty-first century. Results from a Community Climate System Model simulation of twentieth and twenty-first (SRES A1B scenario) century climate show increased winter snowfall (+10–40%), altered maximum snow depth (?5 ± 6 cm), and a shortened snow-season (?14 ± 7 days in spring, +20 ± 9 days in autumn). By conducting a series of prescribed snow experiments with the Community Land Model, we isolate how trends in snowfall, snow depth, and snow-season length affect soil temperature trends. Increasing snowfall, by countering the snowpack-shallowing influence of warmer winters and shorter snow seasons, is effectively a soil warming agent, accounting for 10–30% of total soil warming at 1 m depth and ~16% of the simulated twenty-first century decline in near-surface permafrost extent. A shortening snow season enhances soil warming due to increased solar absorption whereas a shallowing snowpack mitigates soil warming due to weaker winter insulation from cold atmospheric air. Snowpack deepening has comparatively less impact due to saturation of snow insulative capacity at deeper snow depths. Snow depth and snow-season length trends tend to be positively related, but their effects on soil temperature are opposing. Consequently, on the century timescale the net change in snow state can either amplify or mitigate soil warming. Snow state changes explain less than 25% of total soil temperature change by 2100. However, for the latter half of twentieth century, snow state variations account for as much as 50–100% of total soil temperature variations.     

A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

降水相态分离单临界气温模型建立和检验

使用雪日直接界定法,建立了中国大陆长江以北地区（30°N以北）降水相态分离单临界气温统计模型,分东部季风区、西北干燥区和青藏高原区3个不同气候区独立样本建模,检验模型外推使用的可能性,并对单临界气温分离的雨夹雪偏差进行分析。结果表明:所有地区独立样本建立模型估算的单临界气温与根据天气现象记录确定的单临界气温相关性均达到0.05显著性水平,3个气候区独立建模能够估算出降水相态单临界气温的范围及区域特性;以东部季风区和青藏高原区为样本独立建模的估算结果好于西北干燥区;3个独立模型估算的单临界气温偏差绝对值不大于1℃的气象站都多于74%,估算的标准差偏差在-0.5~0.5℃之间的气象站数量占比77%,在-1.0~1.0℃之间的气象站数量占比90%;日平均气温低于单临界气温的雨夹雪日数和降水量与实际降雪日和降雪量的比率北部略小、南部较大,东部季风区的南部雨夹雪界定的雪日和雪量比率均超过100%;使用统计模型确定不同区域雨夹雪中界定的雪日和雪量比率分布也具有可行性。     

Analysis of a Beijing Heavy Snowfall Related to an Inverted Trough in November 2009

This paper studies a heavy snowfall in Beijing that took place on 1 November 2009. The date of the snowfall was about one month earlier than the average. The National Centers for Environmental Prediction (NCEP) reanalysis data, conventional data, and Automatic Weather Station (AWS) data are utilized to explore the reasons for the snowfall and the influencing systems. The main conclusions are as follows: (1) It is revealed from the average geopotential height and average temperature fields at 500 hPa that the large scale circulation in November 2009 was favorable to snowfall. The cold-dry air from West Siberia and the warm-moist air from the Bay of Bengal converged in North China. In addition, it was found from the average moisture flux field at 700 hPa that the main water vapor source was in the Bay of Bengal. (2) Not only the "return current", as usually accepted, but also the inverted trough on the current had an important contribution to the snowfall. The inverted trough could produce the obvious upward motion that is an important environmental condition of snowfalls. (3) More attention should be paid to mesoscale systems such as mesolows during the cold season because of their importance, though they do not occur as frequently as in the warm season. It should be pointed out that AWS data are very useful in mesoscale system analysis during both warm and cold seasons.     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France

In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15–20 km), followed with a statistical downscaling method in order to reach a target resolution of 8 km. The statistical downscaling technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected downscaled temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021–2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ.     

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

21世纪气候变化情景下环北极地区植被生长季与活动积温变化

基于部门间影响模式比较计划（ISI-MIP, Inter-Sectoral Impact Model Intercomparison Project)对CMIP5中5个气候（地球）系统模式模拟结果的降尺度数据,利用多模式集合预估了气候变化情景下21世纪环北极地区植被生长季与活动积温变化。研究发现:1）多模式集合模拟能够基本再现观测的初、终霜日及无霜期长度与>10°C积温的空间分布特征以及1979～2004年各指标变化趋势的空间分布特征,但其对气候变化年际变率的模拟能力较弱;2）至21世纪末,终霜日最多将提前60 d,初霜日将推迟20～40 d,无霜期延长幅度最高可达100 d,积温将增加1000～1200°C。其中RCP8.5情景下,各指标变幅最大,RCP2.6情景下变幅最小;3）各指标变幅呈现出较大的空间差异,亚欧大陆中西部的变幅普遍较大,随着气候变暖,>10°C积温增加幅度表现出明显的纬度地带性,南部增幅较大,北部增幅较小。     

使用岭回归对哈萨克斯坦月平均气温的统计降尺度研究

哈萨克斯坦是世界最大的内陆国家,拥有典型的大陆性气候和多样的地理环境及生态系统,同时哈萨克斯坦的自然环境和人类社会对于气候变化这一全球性问题是敏感的、脆弱的,需要运用科学的研究方法应对气候变化的挑战。通常,区域或局地尺度的气候变化影响研究需要对气候模式输出或再分析资料进行降尺度以获得更细分辨率的气候资料。近年来,大量验证统计降尺度方法在各个地区能力的研究见诸文献,然而在哈萨克斯坦地区验证统计降尺度方法的研究非常少见。本文使用了岭回归的方法对哈萨克斯坦地区11个气象站点1960~2009年的月平均气温进行了统计降尺度研究。结果显示,使用前30年数据和岭回归模型建立大尺度预报因子和观测资料的统计关系可以较好地预测后20年的月平均气温,预测能力在各站各月均有不同程度的差异,地形复杂的站点预测效果较差,夏季预测结果好于冬季;此外,将哈萨克斯坦地区平均来看则与观测数据相吻合。     

Global warming impact on the dominant precipitation processes in the Middle East

In this study, the ability of a regional climate model, based on MM5, to simulate the climate of the Middle East at the beginning of the twenty-first century is assessed. The model is then used to simulate the changes due to global warming over the twenty-first century. The regional climate model displays a negative bias in temperature throughout the year and over most of the domain. It does a good job of simulating the precipitation for most of the domain, though it performs relatively poorly over the southeast Black Sea and southwest Caspian Sea. Using boundary conditions obtained from CCSM3, the model was run for the first and last 5 years of the twenty-first century. The results show widespread warming, with a maximum of ~10 K in interior Iran during summer. It also found some cooling in the southeast Black Sea region during spring and summer that is related to increases in snowfall in the region, a longer snowmelt season, and generally higher soil moisture and latent heating through the summer. The results also show widespread decreases in precipitation over the eastern Mediterranean and Turkey. Precipitation increases were found over the southeast Black Sea, southwest Caspian Sea, and Zagros mountain regions during all seasons except summer, while the Saudi desert region receives increases during summer and autumn. Changes in the dominant precipitation-triggering mechanisms were also investigated. The general trend in the dominant mechanism reflects a change away from the direct dependence on storm tracks and towards greater precipitation triggering by upslope flow of moist air masses. The increase in precipitation in the Saudi desert region is triggered by changes in atmospheric stability brought about by the intrusion of the intertropical convergence zone into the southernmost portion of the domain.     

张家口市崇礼的雪季与冬奥会赛期的降雪特征分析

利用张家口市崇礼1960-2014年逐日降水和天气现象资料,分析了雪季与冬奥会赛期(2月4-20日)的降雪特征。结果显示：崇礼最早降雪初日为10月13日,最迟降雪终日为4月30日,初、终雪日多年平均为11月2日和4月6日;雪季长度最长和最短分别为190 d和123 d,多年平均为156 d。雪季间最长连续无降雪时段多出现在12月末到1月下旬。冬奥会赛期前的11月上旬降雪日数少,但降雪量较大;此后各旬降雪日数、雪量差异不大。2月4-20日间平均4~5 d出现一次降雪,且主要为中小雪,出现大雪的概率极低。这些结果为冬奥会赛场充分利用降雪资源、制定赛事计划和赛事期间的气象条件预测及预报等保障提供参考依据。     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

基于遥感数据的内蒙古草原灌丛物候变化研究

植被物候研究是全球气候变化研究的重要内容,但国际上有关干旱半干旱区灌丛物候变化的研究还很缺乏。为了探讨气候变化对内蒙古草原灌丛物候的影响,利用2000~2011年的MODIS EVI时间序列影像,采用动态阈值法得到6种灌丛12 a物候年际变化情况,结合样点附近气象站的气温和降水数据,分析了灌丛物候与气候变化的动态关系。结果表明：（1）内蒙古中西部草原灌丛返青期、枯黄期都呈现提前的趋势,生长季长度缩短;（2）春季均温升高和前一年秋冬降水增加可以提前灌丛返青期,是影响返青期的主要因素;（3）秋季降水减少和夏秋均温上升都利于枯黄期提前,夏季降水的作用则因灌丛种类不同而略有差异;（4）夏秋均温上升缩短了生长季长度,夏秋降水量、春季均温则多与生长季长度呈正相关。     

Effects of climate change on snow accumulation and melting in the Broye catchment (Switzerland)

The study used a daily step conceptual hydrological model to examine the effects of climate change on snowfall accumulation and on snow cover melting in the Broye catchment (moderate relief- altitude from 400 to 1500 m a.s.l.). Five elevation bands representing a range of climatic conditions were used together with three realistic climate change scenarios based loosely on GCM's predictions and which reflect feasible changes by extending time periods. For a very moderate climate change (rise in air temperature of ca 1 °C), possibly in a near future, the reduction of snow cover duration, mean water equivalent and monthly maximum water equivalent is the most sensitive in the lower part of the catchment and during the first and last months of the snow season. In the higher part of the basin and during the colder months January and February, similar reduction rates can be expected in case of larger climate changes. The floods due to the melting of snow cover are lower. Sometimes rainfall, considered as snow in the present day conditions, generates additional floods during the winter season. For winter sports resorts below 1500 m a.s.l., even the very moderate climatic change scenario (temperature rise around 1 °C) leads to economically very difficult conditions. Finally, a climatic change detection index based on snow cover duration is proposed.     

Assessment of the impact of climate change on the olive flowering in Calabria (southern Italy)

In phenological studies, plant development and its relationship with meteorological conditions are considered in order to investigate the influence of climatic changes on the characteristics of many crop species. In this work, the impact of climate change on the flowering of the olive tree (Olea europaea L.) in Calabria, southern Italy, has been studied. Olive is one of the most important plant species in the Mediterranean area and, at the same time, Calabria is one of the most representative regions of this area, both geographically and climatically. The work is divided into two main research activities. First, the behaviour of olive tree in Calabria and the influence of temperature on phenological phases of this crop are investigated. An aerobiological method is used to determine the olive flowering dates through the analysis of pollen data collected in three experimental fields for an 11-year study period (1999–2009). Second, the study of climate change in Calabria at high spatial and temporal resolution is performed. A dynamical downscaling procedure is applied for the regionalization of large-scale climate analysis derived from general circulation models for two representative climatic periods (1981–2000 and 2081–2100); the A2 IPCC scenario is used for future climate projections. The final part of this work is the integration of the results of the two research activities to predict the olive flowering variation for the future climatic conditions. In agreement with our previous works, we found a significant correlation between the phenological phases and temperature. For the twenty-first century, an advance of pollen season in Calabria of about 9?days, on average, is expected for each degree of temperature rise. From phenological model results, on the basis of future climate predictions over Calabria, an anticipation of maximum olive flowering between 10 and 34?days is expected, depending on the area. The results of this work are useful for adaptation and mitigation strategies, and for making concrete assessments about biological and environmental changes.